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Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission. [ABSTRACT FROM AUTHOR]

Hierarchical networks typically get rated to be not assortative on the basis of the degrees of the end vertices of the edges. However, degree centrality is not cognizant of the location of the nodes in the hierarchy. Rather, we propose the use of hierarchy-aware metrics (such as the k-core index of a node: the largest value of k for which the node would be part of a k-core and not part of any k′-core, where k′ > k and the number of k-core levels for which there exists a non-empty k-core for the network) for assortativity calculations. We first determine the assortativity index ( A s s I kcore ) of a network on the basis of the k-core indexes of the vertices. We then conduct principal component analysis (PCA) on a dataset of the A s s I kcore and the number of k-core levels for a suite of 77 real-world networks. We quantify the hierarchical k-core assortativity of a network as the weighted sum of the entries in the two principal components of this dataset. [ABSTRACT FROM AUTHOR]

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

Swift identification of a set of influential load nodes within power systems is an important task for effective modern power grid operation and security. This paper suggests a suitable approach of Node Strength (NS) or Bus Strength (BS) scheme for effective and swift identification of sets of influential load nodes within power systems. This effective algorithm relies on the Network Structural Characteristics Theory (NSCT) of power systems. The NS scheme uses circuit theory laws to present the linear relationship among the network structure, the voltage and current in the network. The traditional Complex Network Theory (CNT) or Centrality-Based (Degree Centrality (DC), Betweeness Centrality (BC), Closeness Centrality (CC), PageRank Centrality (PRC) and Eigenvector Centrality (EC)) methods for identifying influential load nodes within power systems are also presented in the paper. Thereafter, the numerical values of the NS and each of the CNT-based methods associated with the load nodes, are determined. The relative importance of each load node is then ascertained through ranking (in the order of magnitude or priority) of the values obtained for NS and each of the CNT-based methods. Based on the order of priority, the sets of influential load nodes in the network are identified. Furthermore, performance analyses, using statistical correlation coefficient as well as structural risk assessment, are used to establish the best method suitable for quick identification of a set of influential load nodes in the system. The results obtained are corroborated with the network robustness assessment to further validate the proposed approach. The study uses IEEE 5-bus, IEEE 14-bus and Nigerian 28-bus networks as case studies. The comparison of results obtained show that the NS or BS scheme suggested is more suitable for quick identification of sets of influential load nodes in a relatively large-sized power network. [ABSTRACT FROM AUTHOR]

The real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case. [ABSTRACT FROM AUTHOR]

Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay. [ABSTRACT FROM AUTHOR]

Storage area networks (SANs) are one of the prevalent reliable data storage solutions. However, cascading failures triggered by data overloading have become a major threat to SANs, preventing the desired quality of service from being delivered to users. Based on our preliminary works on studying the impacts of data loading on the reliability performance of SANs, this paper advances the state of the art by implementing node degree-based load redistribution strategies to enhance the SAN reliability, thus mitigating or even preventing the occurrence of cascading failures during the mission time. Load-based and reliability-based node selection rules are considered, which choose nodes with the highest load level and the lowest reliability for load redistribution, respectively. The relationship between data loading and reliability of an individual SAN component is modeled using the accelerated failure-time model with the power law. The SAN reliability is assessed using a combinatorial decision diagram-based approach. The application and effectiveness of the proposed load redistribution strategies are demonstrated and compared through a case study of an SAN with the mesh topology.

Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay.

Network reliability reflects a system's ability to perform specified functions under specified topological and traffic conditions. Network reliability is the weighted sum of connection reliability and travel time reliability. Based on complex network theory, a new method was proposed to calculate the travel time reliability of road networks. The topology model of a regional highway network in China was built using the dual method. After a random attack or deliberate attack, node sizes in the sub-network can be used to reflect the node importance for network connection reliability. Some conclusions were drawn after the change in travel time coefficient and delay coefficient. The increases in the two coefficients will accelerate the decrease of travel time reliability of the highway network. After a comparison among three methods of travel time reliability, including variation coefficient, the misery indexes, and the new equation, the new method was further verified. The influence factors of highway network reliability were analyzed under the condition of different highway blockage and congestion. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is an auto‐configured and baseless wireless network system to detect the physical scenarios or atmospheric situations like temperature, sound, pressure, pollutants, and so on. These investigations can be aided for the smooth transmission of data along with the networks. The current smart world exploits WSN in most applications, such as technical applications, industrial applications, low‐power embedded systems, and other smart communications. Even though it has so many applications, the available limited supply resource causes ill effects while transmitting large amounts of data. Hence, we can deem energy efficiency as a crucial factor that affects the performances of WSN. Processes such as routing, and for long‐lasting life span WSN requires more energy. Hence, to have an effective routine and enhanced life span of WSN, we propose a novel energy‐efficient routing protocol based on a hybrid improved whale artificial ecosystem optimization algorithm. To attain an energy‐efficient network it is ineluctable to choose an optimized cluster head and other nodes. These can be performed by evaluating various fitness functions of WSN. Henceforth, the nodes are organized and better routing can be obtained based on the fitness function values. This also improves the energy efficiency of the WSN and has been utilized to select the other forwarding nodes. Experimental analyses are conducted based on the deemed three scenarios which are based on the positioning of the base station (BS) and compared with other prior works. In all aspects, our proposed work has better performances than the other state‐of‐art works. [ABSTRACT FROM AUTHOR]

Summary: Mobile ad hoc network (MANET) is an infrastructure‐less network. Every Node act as a sender as well as receiver while sending a packet. Neighbor coverage probabilistic rebroadcasting (NCPR) protocol plays a momentous role in MANET for determining a route by helping the transitional nodes to onward a packet when the mobile nodes are not in the transmission space. This protocol minimizes the interference among node and increases the network capacity. There occurs a delay in the network while sending a packet via failure route or channel. Before sending a packet in the network, all the neighbor nodes have to be verified to avert congestion in the network. Quality of service (QoS) plays an important role in the development of MANET. In this research work, delay and neighbor connectivity‐based constrain is focused. The impact of node mobility during interference and delay is examined using network coding‐based topology control algorithm. By using this algorithm, irrelevant links are identified and detached from the topology. The simulation result shows that neighbor coverage established topology control algorithm for delay (NCTCAD) can reduce the delay and upturn the performance in MANETs. [ABSTRACT FROM AUTHOR]

Recently time series prediction based on network analysis has become a hot research topic. However, how to more accurately forecast time series with good efficiency is still an open question. To address this issue, we propose an efficient time series forecasting method based on the DC algorithm and visibility relations on the vertexes set. Firstly, the time series is mapped into the network by the DC algorithm, which is a more efficient approach to generate the visibility graph. Then, we use the variation trends (slope) of those nodes that have visibility relation with the last node to get the preliminary predictive values. Afterward, the value of the last node is adopted to obtain the revised predictive values, which are assigned different weights according to node degree and time distance to get the final weighted result. To better demonstrate the prediction performance and applicability of the proposed method, the proposed method is applied to different time series data sets. The empirical results show that the proposed method could provide a higher level of forecasting accuracy than many methods with relatively lower time complexity.

For the analysis of urban networks indicators, like average node degree, connectivity and betweenness centrality, are widely used. Their values are calculated for a catchment area of the network. This research investigates if and to what extent the size of the catchment area influences the indicator's values. A methodology for determining the size of catchment area is proposed and recommendations for its appropriate size for pedestrian network analysis are offered. For the mathematical deduction of the relationship between the size of the catchment area and the indicator values an idealized regular network is used as a reference model. We found that the size effect on indicator values is prominent. It decreases exponentially as the size of the catchment area increases. The size effect is notable until a side length of the catchment area that equals 50 times the average street length, while the variation is still acceptable if it is 30 to 40 times the average street length. The typical average street length in cities is about 100 meters, a catchment area larger than 4000 x 4000m² is not necessary. This reference model can also be used to evaluate real networks and to compare them with an idealized case. [ABSTRACT FROM AUTHOR]

Content-centric networking (CCN) becomes an imminent Internet architecture that emphasizes the data-centric approach for information retrieval instead of searching for the hosts in the network. CCN offers data (content) caching and its distribution capabilities to reduce average latency, the load of data servers, and improve network bandwidth utilization during content delivery. In this direction, the performance of node degree centrality and distance-based autonomous caching strategies have been investigated. Then, a novel content caching strategy called content placement based on normalized node degree and distance (CPNDD) is proposed. The CPNDD scheme considers content provider distance and node degree centrality parameters together to select the optimal cache locations for comprehensive utilization of the available caching capacities and a further reduction in average latency during content retrieval. The weights for these caching parameters have been determined via widespread simulations on the Abilene networks. The proposed caching scheme is implemented and tested in the simulated environment of ndnSIM and compared with peer competing schemes in CCN. The execution outcomes are examined for different caching capacities (50 and 100), Zipf popularity skewness factors (0.7 and 1.0) and request rates (50/s and 100/s). The simulation executions illustrate that CPNDD caching strategy escalates the cache hit probability and hop-reduction ratio up-to 8 % and 9 % as compared to existing schemes. Hence, the proposed content placement scheme improves the performance of CCN networks considerably and makes it suitable for the applications of Industry 4.0. [ABSTRACT FROM AUTHOR]

Survivable virtual optical network (VON) mapping is researched to overcome the ossification of current network architectures. In this paper, we study the survivable VON mapping problem in elastic optical network (EON) with the objective of minimizing the average resource consumption of VON request. We propose a protective algorithm, namely, coordinated mapping algorithm based on minimum spanning tree (CMST), to provide the dedicated protection against the single physical link failure. In CMST, for virtual node mapping, we not only consider the resources constraint but also the node degree constraint. For virtual link mapping, we adopt the coordinated mapping method for part of the virtual nodes and virtual links. And we provide the backup path for the minimum spanning tree link of virtual topology to reduce the resource consumption. Simulation results showed that CMST can reduce the VON request blocking probability and average network resource consumption. And it can increase the revenue of physical network. [ABSTRACT FROM AUTHOR]

Link prediction based on traditional models have attracted many interests recently. Among all models, the ones based on topological similarity have achieved great success. However, researchers pay more attention to links, but less to endpoint influence. After profound investigation, we find that the synthesis of degree and H-index plays an important role in modeling endpoint influence. So, in this paper, we propose link prediction models based on weighted synthetical influence, exploring the role of H-index and degree in endpoint influence measurement. Experiments on 12 real-world networks show that the proposed models can provide higher accuracy. [ABSTRACT FROM AUTHOR]

It is crucial to identify the important nodes in the network. On the basis of the K-shell algorithm, this study researched the recognition of important nodes in complex networks. The study introduces concepts of edge weight and influence coefficient, the design of an IKS algorithm, and an analysis of its recognition effect in Zachary network and real micro blog network. The results showed that the partition results of the K-shell algorithm were coarse, while the partition results of the IKS algorithm were refined; the IKS algorithm could sort the important nodes accurately on the basis of the K-shell algorithm, and its rationality was higher than that of the closeness centralization and PaperRank algorithm. The partition results in the microblog network also verified the effectiveness of the improved method. The experimental results show that the IKS algorithm is reliable in the important node identification, which is beneficial to the recognition of important nodes in complex network. [ABSTRACT FROM AUTHOR]

Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.

Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification of its etiological origin are urgent. Bursts within the lateral habenula (LHb) drive depression in rats, but whether dysfunctional LHb is associated with SD in human is unknown. Here we develop connectome-based biomarkers which predict SD and identify dysfunctional brain regions and connections. T1 weighted images and resting-state functional MRI (fMRI) data were collected from 34 subjects with SD and 40 healthy controls (HCs). After the brain is parcellated into 48 brain regions (246 subregions) using the human Brainnetome Atlas, the functional network of each participant is constructed by calculating the correlation coefficient for the time series of fMRI signals of each pair of subregions. Initial candidates of abnormal connections are identified by the two-sample t-test and input into Support Vector Machine models as features. A total of 24 anatomical-region-based models, 231 sliding-window-based models, and 100 random-selection-based models are built. The performance of these models is estimated through leave-one-out cross-validation and evaluated by measures of accuracy, sensitivity, confusion matrix, receiver operating characteristic curve, and the area under the curve (AUC). After confirming the region with the highest accuracy, subregions within the thalamus and connections associated with subregions of LHb are compared. It is found that five prediction models using connections of the thalamus, posterior superior temporal sulcus, cingulate gyrus, superior parietal lobule, and superior frontal gyrus achieve an accuracy >0.9 and an AUC >0.93. Among 90 abnormal connections associated with the thalamus, the subregion of the right posterior parietal thalamus where LHb is located has the most connections (n = 18), the left subregion only has 3 connections. In SD group, 10 subregions in the thalamus have significantly different node degrees with those in the HC group, while 8 subregions have lower degrees ( p < 0.01), including the one with LHb. These results implicate abnormal brain connections associated with the thalamus and LHb to be associated with SD. Integration of these connections by machine learning can provide connectome-based biomarkers to accurately diagnose SD.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/)., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification of its etiological origin are urgent. Bursts within the lateral habenula (LHb) drive depression in rats, but whether dysfunctional LHb is associated with SD in human is unknown. Here we develop connectome-based biomarkers which predict SD and identify dysfunctional brain regions and connections. T1 weighted images and resting-state functional MRI (fMRI) data were collected from 34 subjects with SD and 40 healthy controls (HCs). After the brain is parcellated into 48 brain regions (246 subregions) using the human Brainnetome Atlas, the functional network of each participant is constructed by calculating the correlation coefficient for the time series of fMRI signals of each pair of subregions. Initial candidates of abnormal connections are identified by the two-sample t -test and input into Support Vector Machine models as features. A total of 24 anatomical-region-based models, 231 sliding-window-based models, and 100 random-selection-based models are built. The performance of these models is estimated through leave-one-out cross-validation and evaluated by measures of accuracy, sensitivity, confusion matrix, receiver operating characteristic curve, and the area under the curve (AUC). After confirming the region with the highest accuracy, subregions within the thalamus and connections associated with subregions of LHb are compared. It is found that five prediction models using connections of the thalamus, posterior superior temporal sulcus, cingulate gyrus, superior parietal lobule, and superior frontal gyrus achieve an accuracy >0.9 and an AUC >0.93. Among 90 abnormal connections associated with the thalamus, the subregion of the right posterior parietal thalamus where LHb is located has the most connections (n = 18), the left subregion only has 3 connections. In SD group, 10 subregions in the thalamus have significantly different node degrees with those in the HC group, while 8 subregions have lower degrees (p < 0.01), including the one with LHb. These results implicate abnormal brain connections associated with the thalamus and LHb to be associated with SD. Integration of these connections by machine learning can provide connectome-based biomarkers to accurately diagnose SD. [ABSTRACT FROM AUTHOR]

People like to communicate with others who have similar opinions in daily life. "Open" individuals are willing to accept peers' opinions, whereas "conservative" individuals are more closed to others' opinions. Based on this reality, an opinion dynamic model is proposed with a time-varying trust threshold related to the node degree, which refers to the number of individuals that can be communicated directly. Similar opinions within the trust thresholds at the beginning will eventually form a consensus opinion. Individuals who have common neighbors converge to consensus faster than ones who have no common neighbors. The network size impacts the convergent speed of the opinion cluster and the trust threshold cluster. Individuals in small groups are more stressed than in large ones when they try to insist their opinions. A group with half of the individuals categorized as doubting and the other half categorized as trusting exhibits consistent confidence but experiences difficulty in reaching a consensus: "harmony but difference" may be a criterion for unifying a group. [ABSTRACT FROM AUTHOR]

Recently fuzzy based hierarchical routing protocols such as F-MCHEL and F-SCH were proposed to improve the lifetime of a wireless sensor network. In certain cases F-MCHEL results in several small sized clusters increasing the inter cluster communication cost. This is due to the negligence of node degree in cluster head selection. The performance of F-SCH is observed to be better than F-MCHEL because super cluster head selection is based on centrality and mobility. Though it minimized inter cluster energy, the randomness in LEACH based cluster head selection inhibits the benefits of multilevel routing. Hence, with an objective to minimize the total energy consumed by the network, fuzzy based two-level hierarchical protocol is proposed. The objective is achieved by appropriate selection of cluster head and super cluster head. Cluster head selection is based on battery power, centrality and node degree while super cluster head selection is based on battery power, centrality and mobility of cluster heads. Experiments are carried out both in simulation and hardware to validate the proposal. The performance of the network in terms of first node, half node and last node death is studied and the proposal is compared with F-MCHEL and F-SCH. The results reveal the efficiency of the proposal. [ABSTRACT FROM AUTHOR]

Refined topological relations play an important role in spatial database quality control. Currently, there is no unified and reasonable method to represent refined line/region and line/line topological relations in two-dimensional (2D) space. In addition, the existing independent line/region and line/line models have some drawbacks such as incomplete type discrimination and too many topological invariants. In this paper, a refined line/region and line/line topological relations are represented uniformly by the sequence, dimension, and topological type of the intersection components. To make the relevant definitions conform to the traditional cognitions in 2D Euclidean space, the (simple) spatial object is defined based on manifold topology, and the spatial intersection components are defined based on the whole-whole object intersection set. Then the topological invariant of node degree is introduced, and the adjacent point kinds (e.g., “Null”, “On”, “In”, and “Out”) are defined to distinguish the intersection component types. Excluding impossible and symmetrical types, 29 types of intersection-lines (including 21 between lines/regions and 8 between lines/lines), and 6 types of intersection-points (including 2 between lines/regions and 4 between lines/lines) are classified. On this basis, a node degree-based whole-whole object intersection sets (N-WWIS) model for refined line/region and line/line topological relations is presented, and it can be combined with the Euler number-based whole object intersection and difference (E-WID) model (coarse level) to form a hierarchical representation method of topological relations. Furthermore, a prototype system based on the N-WWIS model for automatic topological integrity checking is developed and some evaluation experiments are conducted with OpenStreetMap (OSM) data is presented based on the classification of intersection components. The experimental results show that the N-WWIS model will enable the geographic information systems (GIS) community to develop automated topological conflict checking and dealing tools for spatial data updates and quality control.

In present study, I proposed a node degree dependent random perturbation algorithm for prediction of missing links in the network. In the algorithm, I assume that a node with more existing links harbors more missing links. There are two rules. Rule 1 means that a randomly chosen node tends to connect to the node with greater degree. Rule 2 means that a link tends to be created between two nodes with greater degrees. Missing links of some tumor related networks (pathways) are predicted. The results prove that the prediction efficiency and percentage of correctly predicted links against predicted missing links with the algorithm increases as the increase of network complexity. The required number for finding true missing links in the predicted list reduces as the increase of network complexity. Prediction efficiency is complexity-depedent only. Matlab codes of the algorithm are given also. Finally, prospect of prediction for missing links is briefly reviewed. So far all prediction methods based on static topological structure only (represented by adjacency matrix) seems to be low efficient. Network evolution based, node similarity based, and sampling based (correlation based) methods are expected to be the most promising in the future.

Properly adjusting the transmission power of the wireless sensor nodes has shown to be an effective approach to reduce energy consumption and to improve the network reliability. In this paper, an adaptive Node Degree adjustment based Power Control (NDPC) method for wireless sensor networks is proposed to dynamically adjust the transmission power of the nodes. In NDPC, each node is embedded with a fuzzy neural controller which is used to adjust the target node degree so as to control the communication range properly. The fuzzy neural controller consists of two Fuzzy Inference Systems (FIS). One adopts closed-loop feedback mechanism to adjust the target node degree according to the residual energy. The other adjusts the communication range by a self-learning neural network to control the transmission power based on the target node degree. Consequently, the actual energy consumption of the node is reduced while keeping the desired node degree. Several simulation experiments are conducted to evaluate the performance of NDPC, and the results show that NDPC can reduce the actual energy consumption as well as extend the network lifetime. [ABSTRACT FROM AUTHOR]

The test path of solder joint intermittent connection faults under direct-current stimulus is examined in this paper. According to the physical structure of the circuit, a network model is established first. A network node is utilised to represent the test node. The path edge refers to the number of intermittent connection faults in the path. Then, the selection criteria of the test path based on the node degree index are proposed and the solder joint intermittent connection faults are covered using fewer test paths. Finally, three circuits are selected to verify the method. To test if the intermittent fault is covered by the test paths, the intermittent fault is simulated by a switch. The results show that the proposed method can detect the solder joint intermittent connection fault using fewer test paths. Additionally, the number of detection steps is greatly reduced without compromising fault coverage. [ABSTRACT FROM AUTHOR]

For efficient running of wireless sensor network applications, energy conservation of the sensors becomes a prime paradigm for prolonging lifetime of the network. Taking this aspect into consideration, a cluster head weight selection method called Cluster Chain Weight Metrics approach (CCWM) has been discussed that takes service parameters for enhancing performance of the overall network. In a clustering based approach one of the main concerns is selection of appropriate cluster heads in the network and the formation of balanced clusters. Cluster heads are selected first in a network based on weight metric and then cluster formation takes place. This approach not only aims to conserve energy of sensors but also balances load. A local clustering mechanism is adopted within the cluster to reduce computation and communication cost. Also, a new technique for data transmission is explored. The results of the proposed approach are compared through simulation with LEACH, WCA and IWCA. The proposed approach shows an improvement on an average over rounds by 51% over LEACH, 27% from WCA and 18.8% from IWCA in terms of lifetime and energy consumption.

Social networking has been a feature of human society. From the early hunter-gatherer tribes, medieval guilds, the twentieth century workplaces, up to online entities like Facebook and Twitter, it is difficult to think of a time or place where all people did not belong to at least one cooperative group. It follows that social network formation has been studied extensively in the past decades and will continue to be a popular area of research. Past research has primarily confined itself to considering cases in which new members are introduced into the networks by making a constant number of connections to those who are already present in the networks. Our study aims to fill the glaring gap in the variety of network formation modelling. Most notably, we want to consider scenarios in which the number of connections new members make to those already present in the networks is determined by chance. More specifically, the number of connections made to existing members when a new one is introduced into the network is characterized by a positive integer-valued random variable. The objective of the study is to determine the distribution of degree of a node in this kind of social networks. It is determined that the node degree distribution is a mixture of geometric distributions. Three numerical examples are provided in the study to demonstrate the validity of our findings. [ABSTRACT FROM AUTHOR]

Population biomass, a key concept in traditional ecology, plays a vital role in assessing the consequences of biodiversity loss, in terms of community structure and ecosystem processes. Recent studies derived from network analysis assumed node degree, the number of trophic links of a focal node, as an indicator of node importance in maintaining stability, yet largely ignored the role of biomass. Here, we focus on this underappreciated concept and test if biomass can be used to identify node importance, and compare it with the more commonly applied indicator – node degree. The dynamical approaches were used to simulate secondary extinctions after sequential deletion of nodes in order of highest degree, biomass or random assignment. We showed that biomass-based deletions caused more collateral losses and thus led to lower stability than random removals in the dynamical analyses, i.e. biomass was an efficient indicator. However, the traditional indicator degree failed in identifying node importance. Our findings indicated the overstatement of the importance of node degree and emphasized the importance of population biomass in food webs, and supported a new view in analyzing the stability of food webs to species loss. [ABSTRACT FROM AUTHOR]

Co-expression networks may provide insights into the patterns of molecular interactions that underlie cellular processes. To obtain a better understanding of miRNA expression patterns in gastric adenocarcinoma and to provide markers that can be associated with histopathological findings, we performed weighted gene correlation network analysis (WGCNA) and compare it with a supervised analysis. Integrative analysis of target predictions and miRNA expression profiles in gastric cancer samples was also performed. WGCNA identified a module of co-expressed miRNAs that were associated with histological traits and tumor condition. Hub genes were identified based on statistical analysis and network centrality. The miRNAs 100, let-7c, 125b and 99a stood out for their association with the diffuse histological subtype. The 181 miRNA family and miRNA 21 highlighted for their association with the tumoral phenotype. The integrated analysis of miRNA and gene expression profiles showed the let-7 miRNA family playing a central role in the regulatory relationships. [ABSTRACT FROM AUTHOR]

Visual rhythmic stimulation evokes a robust power increase exactly at the stimulation frequency, the so-called steady-state response (SSR). Localization of visual SSRs normally shows a very focal modulation of power in visual cortex and led to the treatment and interpretation of SSRs as a local phenomenon. Given the brain network dynamics, we hypothesized that SSRs have additional large-scale effects on the brain functional network that can be revealed by means of graph theory. We used rhythmic visual stimulation at a range of frequencies (4–30 Hz), recorded MEG and investigated source level connectivity across the whole brain. Using graph theoretical measures we observed a frequency-unspecific reduction of global density in the alpha band “disconnecting” visual cortex from the rest of the network. Also, a frequency-specific increase of connectivity between occipital cortex and precuneus was found at the stimulation frequency that exhibited the highest resonance (30 Hz). In conclusion, we showed that SSRs dynamically re-organized the brain functional network. These large-scale effects should be taken into account not only when attempting to explain the nature of SSRs, but also when used in various experimental designs. [ABSTRACT FROM AUTHOR]

Background: To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility. Results: In this article, we propose a regularized logistic regression model. Different from previous methods which focus on individual genes or modules, our model takes gene pairs, which are connected in a protein-protein interaction network, into account. A line graph is constructed to represent the adjacencies between pairwise interactions. Based on this line graph, we incorporate the degree information in the model via an adaptive elastic net, which makes our model less dependent on the expression data. Experimental results on six publicly available breast cancer datasets show that our method can not only achieve competitive performance in classification, but also retain great stability in variable selection. Therefore, our model is able to identify the diagnostic and prognostic biomarkers in a more robust way. Moreover, most of the biomarkers discovered by our model have been verified in biochemical or biomedical researches. Conclusions: The proposed method shows promise in the diagnosis of disease pathogenesis with different clinical characteristics. These advances lead to more accurate and stable biomarker discovery, which can monitor the functional changes that are perturbed by diseases. Based on these predictions, researchers may be able to provide suggestions for new therapeutic approaches. [ABSTRACT FROM AUTHOR]

As a branch of topology control, power control is of great importance to prolong the survival time of the network. A transmission power control algorithm is proposed under the minimum connected average node degree constraint in this paper. Using a Poisson point process in two dimensions, we derive an analytical expression that determines the required transmission range to achieve a specified connectivity probability for a given node density, and then the minimum connected average node degree is obtained. Based on the above results, an algorithm named MCAND is designed to achieve the given connected probability by adjusting the transmission power of each node dynamically, with the node degree equal to the minimum connected average node degree. Simulation results show that the performance of our algorithm is superior to that of the related algorithms based on node degree in terms of coverage and connectivity. [ABSTRACT FROM AUTHOR]

For efficient running of wireless sensor network applications, energy conservation of the sensors becomes a prime paradigm for prolonging lifetime of the network. Taking this aspect into consideration, a cluster head weight selection method called Cluster Chain Weight Metrics approach (CCWM) has been discussed that takes service parameters for enhancing performance of the overall network. In a clustering based approach one of the main concerns is selection of appropriate cluster heads in the network and the formation of balanced clusters. Cluster heads are selected first in a network based on weight metric and then cluster formation takes place. This approach not only aims to conserve energy of sensors but also balances load. A local clustering mechanism is adopted within the cluster to reduce computation and communication cost. Also, a new technique for data transmission is explored. The results of the proposed approach are compared through simulation with LEACH, WCA and IWCA. The proposed approach shows an improvement on an average over rounds by 51% over LEACH, 27% from WCA and 18.8% from IWCA in terms of lifetime and energy consumption. [ABSTRACT FROM AUTHOR]

An increasing amount of studies apply repetetive transcranial magnetic stimulation (rTMS) to treat chronic tinnitus, yet the neurophysiological impacts have remained largely obscure. Several studies show that endogenous brain rhythms may be enhanced via diverse brain stimulation techniques applying rhythmic stimulation. Here, we investigated in normal hearing participants whether application of rTMS (left auditory cortex) with an individualized alpha frequency was capable of increasing alpha activity in stimulated auditory regions in a sustained manner. Behavioral intensity discrimination performance worsened for the rTMS group as compared to Sham. Electroencephalography (EEG) data, however, clearly show that this functional inhibition is not accompanied by increases of auditory cortical alpha. Even though more pronounced power reductions for the rTMS group were observed at slower frequencies (delta to theta range) at stimulated and other sites of the left hemisphere, they were unrelated to behavioral changes. Our results also strongly suggest that the amount of power modulations at these slower frequencies is strongly dependent on pre-rTMS power, thus supporting current state-dependency notions. Strong relationships to behavioral changes were in particular observed for anterior cingulate cortex (ACC) beta power and posterior cingulate cortex (PCC) beta connectivity. Moreover, these beta band measures were strongly inter-related and when viewed together specifically sensitive to behavioral changes in the rTMS group. We conclude that currently alpha frequency rTMS is not a promising avenue for the treatment of chronic tinnitus and that beneficial effects could be mediated via nonauditory systems. Our study argues for the value of combined EEG-TMS studies when investigating the impacts of rTMS. Hum Brain Mapp 35:14-29, 2014. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A fundamental problem that confronts wireless networks are localization, mobility maintenance and number of neighbors required to maintain the connectivity. To overcome this problem and achieve a better quality of service, a self stability model is introduced, named as localized tree model which includes min and max routing methods. This work is based on node degree, neighbor's information and coverage area. Based on the mobility requirements of the network, dynamic structures are formed with minimum control load and complexity. Main objective of the research is to obtain the network parameters from the connectivity analysis. The performance of the proposed approach is witnessed by analyzing the parameters like scalability, packet delivery ratio and connectivity efficiency. [ABSTRACT FROM AUTHOR]

We investigate the mean degree and the isolation probability of a network node sustaining intrasystem interference and intersystem interference from multiple coexisting wireless ad hoc networks. Under general fading of the desired signal and interfering signals, a closed-form upper bound on the mean node degree and a lower bound on the node isolation probability are obtained by thoroughly considering dominating interferers and second-order interferers. A closed-form lower bound on the mean node degree is further computed. The ratio between the upper bound and lower bound is bounded, and they are asymptotically tight as the path-loss exponent approaches infinity. Moreover, a nearly closed-form expression for the mean node degree is derived in terms of inverse Laplace transform of the fading distribution of the desired signal. Our derived local network quantities could find application in the approximate study of a number of metrics related to connectivity and delay of information propagation in wireless ad hoc networks. [ABSTRACT FROM PUBLISHER]

Background: Physiological left ventricular hypertrophy (LVH) involves complex cardiac remodeling that occurs as an adaptive response to chronic exercise. A stark clinical contrast exists between physiological LVH and pathological cardiac remodeling in response to diseases such as hypertension, but little is known about the precise molecular mechanisms driving physiological adaptation. Results: In this study, the first large-scale analysis of publicly available genome-wide expression data of several in vivo murine models of physiological LVH was carried out using network analysis. On evaluating 3 million gene co-expression patterns across 141 relevant microarray experiments, it was found that physiological adaptation is an evolutionarily conserved processes involving preservation of the function of cytochrome c oxidase, induction of autophagy compatible with cell survival, and coordinated regulation of angiogenesis. Conclusion: This analysis not only identifies known biological pathways involved in physiological LVH, but also offers novel insights into the molecular basis of this phenotype by identifying key networks of co-expressed genes, as well as their topological and functional properties, using relevant high-quality microarray experiments and network inference. [ABSTRACT FROM AUTHOR]